





_a     :    Union[str, Any]         =						'\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a     :    List[Any]         =						[{'type': 'code', 'content': INSTALL_CONTENT}]
_a     :    str         =						{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}






'''simple docstring'''







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase_     						=				logging.get_logger(__name__)  # pylint: disable=invalid-name







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if not path:
      return "pipe"

   for ext in PipelineDataFormat.SUPPORTED_FORMATS:
      if path.endswith(UpperCAmelCase						):
         return ext

   raise Exception(
       F"""Unable to determine file format from file extension {path}. """
       F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     pipeline(
       task=args.task      ,model=args.model if args.model else None      ,config=args.config      ,tokenizer=args.tokenizer      ,device=args.device      ,)
   A__       =     try_infer_format_from_ext(args.input						) if args.format == 'infer' else args.format
   A__       =     PipelineDataFormat.from_str(
       format=UpperCAmelCase      ,output_path=args.output      ,input_path=args.input      ,column=args.column if args.column else nlp.default_input_names      ,overwrite=args.overwrite      ,)
   return RunCommand(UpperCAmelCase      ,UpperCAmelCase						)







class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  Tuple    ,		a       :  Pipeline    ,		a       :  PipelineDataFormat    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     nlp
    A__       =     reader






 @staticmethod
 def 						_UpperCamelCase (a       :  ArgumentParser    )       ->   str:






    """simple docstring"""


    A__       =     parser.add_parser('run'    ,		help='Run a pipeline through the CLI'    )
    run_parser.add_argument('--task'    ,		choices=get_supported_tasks()    ,		help='Task to run'    )
    run_parser.add_argument('--input'    ,		type=a    ,		help='Path to the file to use for inference'    )
    run_parser.add_argument('--output'    ,		type=a    ,		help='Path to the file that will be used post to write results.'    )
    run_parser.add_argument('--model'    ,		type=a    ,		help='Name or path to the model to instantiate.'    )
    run_parser.add_argument('--config'    ,		type=a    ,		help='Name or path to the model\'s config to instantiate.'    )
    run_parser.add_argument(
        '--tokenizer'    ,		type=a    ,		help='Name of the tokenizer to use. (default: same as the model name)'    )
    run_parser.add_argument(
        '--column'    ,		type=a    ,		help='Name of the column to use as input. (For multi columns input as QA use column1,columns2)'    ,		)
    run_parser.add_argument(
        '--format'    ,		type=a    ,		default='infer'    ,		choices=PipelineDataFormat.SUPPORTED_FORMATS    ,		help='Input format to read from'    ,		)
    run_parser.add_argument(
        '--device'    ,		type=a    ,		default=-1    ,		help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'    ,		)
    run_parser.add_argument('--overwrite'    ,		action='store_true'    ,		help='Allow overwriting the output file.'    )
    run_parser.set_defaults(func=a    )






 def 						_UpperCamelCase (self       :  int    )       ->   Optional[Any]:






    """simple docstring"""


    A__  ,			A__       =     self._nlp, []

    for entry in self._reader:
       A__       =     nlp(**a    ) if self._reader.is_multi_columns else nlp(a    )
       if isinstance(a    ,		a    ):
          outputs.append(a    )
       else:
          outputs += output

        # Saving data
    if self._nlp.binary_output:
       A__       =     self._reader.save_binary(a    )
       logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""    )
    else:
       self._reader.save(a    )


